Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations34603
Missing cells13118
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 MiB
Average record size in memory232.0 B

Variable types

Text10
Categorical9
Numeric8
DateTime2

Alerts

db_code_commune is highly overall correlated with db_code_dpt and 1 other fieldsHigh correlation
db_code_dpt is highly overall correlated with db_code_commune and 1 other fieldsHigh correlation
iso_pays is highly overall correlated with db_code_commune and 1 other fieldsHigh correlation
db_continent is highly imbalanced (92.4%) Imbalance
dd_continent is highly imbalanced (93.1%) Imbalance
iso_date is highly imbalanced (76.0%) Imbalance
db_lib_commune has 481 (1.4%) missing values Missing
db_dept_isocode_3166 has 5472 (15.8%) missing values Missing
dd_lib_commune has 691 (2.0%) missing values Missing
dd_dept_isocode_3166 has 6334 (18.3%) missing values Missing
age has 12083 (34.9%) zeros Zeros
distance has 12051 (34.8%) zeros Zeros

Reproduction

Analysis started2025-05-31 10:10:35.573687
Analysis finished2025-05-31 10:10:53.837471
Duration18.26 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

nom
Text

Distinct22640
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
2025-05-31T12:10:54.221854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length29
Mean length7.0940959
Min length2

Characters and Unicode

Total characters245477
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18048 ?
Unique (%)52.2%

Sample

1st rowLETERME
2nd rowDEROUT
3rd rowGRAS Y PLASSARD
4th rowLENOIR
5th rowLESZCZUK
ValueCountFrequency (%)
le 364
 
1.0%
de 192
 
0.5%
martin 102
 
0.3%
simon 54
 
0.2%
robert 53
 
0.1%
durand 50
 
0.1%
bernard 50
 
0.1%
thomas 49
 
0.1%
richard 48
 
0.1%
laurent 48
 
0.1%
Other values (22591) 34807
97.2%
2025-05-31T12:10:54.968536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 29772
12.1%
A 24314
 
9.9%
R 21706
 
8.8%
I 17138
 
7.0%
O 16412
 
6.7%
L 15965
 
6.5%
N 15908
 
6.5%
U 12845
 
5.2%
T 11917
 
4.9%
S 10886
 
4.4%
Other values (19) 68614
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 245477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 29772
12.1%
A 24314
 
9.9%
R 21706
 
8.8%
I 17138
 
7.0%
O 16412
 
6.7%
L 15965
 
6.5%
N 15908
 
6.5%
U 12845
 
5.2%
T 11917
 
4.9%
S 10886
 
4.4%
Other values (19) 68614
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 245477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 29772
12.1%
A 24314
 
9.9%
R 21706
 
8.8%
I 17138
 
7.0%
O 16412
 
6.7%
L 15965
 
6.5%
N 15908
 
6.5%
U 12845
 
5.2%
T 11917
 
4.9%
S 10886
 
4.4%
Other values (19) 68614
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 245477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 29772
12.1%
A 24314
 
9.9%
R 21706
 
8.8%
I 17138
 
7.0%
O 16412
 
6.7%
L 15965
 
6.5%
N 15908
 
6.5%
U 12845
 
5.2%
T 11917
 
4.9%
S 10886
 
4.4%
Other values (19) 68614
28.0%

prenom
Text

Distinct20813
Distinct (%)60.4%
Missing138
Missing (%)0.4%
Memory size270.5 KiB
2025-05-31T12:10:55.435355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length40
Mean length13.850689
Min length1

Characters and Unicode

Total characters477364
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18030 ?
Unique (%)52.3%

Sample

1st rowPATRICE,EUGENE,OMER
2nd rowJEAN,DANIEL
3rd rowBORIS,GUY
4th rowVALERIE,PAULE
5th rowMARIE-AGNES
ValueCountFrequency (%)
jean 165
 
0.5%
david 148
 
0.4%
marie 143
 
0.4%
joseph 130
 
0.4%
michel 124
 
0.4%
nathalie 118
 
0.3%
alain 108
 
0.3%
christophe 104
 
0.3%
philippe 102
 
0.3%
laurent 100
 
0.3%
Other values (20805) 33255
96.4%
2025-05-31T12:10:56.154388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 75659
15.8%
A 48033
 
10.1%
I 42253
 
8.9%
R 39327
 
8.2%
N 36251
 
7.6%
, 34079
 
7.1%
L 28244
 
5.9%
C 18815
 
3.9%
S 17724
 
3.7%
O 16528
 
3.5%
Other values (20) 120451
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 477364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 75659
15.8%
A 48033
 
10.1%
I 42253
 
8.9%
R 39327
 
8.2%
N 36251
 
7.6%
, 34079
 
7.1%
L 28244
 
5.9%
C 18815
 
3.9%
S 17724
 
3.7%
O 16528
 
3.5%
Other values (20) 120451
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 477364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 75659
15.8%
A 48033
 
10.1%
I 42253
 
8.9%
R 39327
 
8.2%
N 36251
 
7.6%
, 34079
 
7.1%
L 28244
 
5.9%
C 18815
 
3.9%
S 17724
 
3.7%
O 16528
 
3.5%
Other values (20) 120451
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 477364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 75659
15.8%
A 48033
 
10.1%
I 42253
 
8.9%
R 39327
 
8.2%
N 36251
 
7.6%
, 34079
 
7.1%
L 28244
 
5.9%
C 18815
 
3.9%
S 17724
 
3.7%
O 16528
 
3.5%
Other values (20) 120451
25.2%

sexe
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
M
21164 
F
13439 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34603
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 21164
61.2%
F 13439
38.8%

Length

2025-05-31T12:10:56.331087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T12:10:56.449270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 21164
61.2%
f 13439
38.8%

Most occurring characters

ValueCountFrequency (%)
M 21164
61.2%
F 13439
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 21164
61.2%
F 13439
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 21164
61.2%
F 13439
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 21164
61.2%
F 13439
38.8%

age
Real number (ℝ)

Zeros 

Distinct97
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.350779
Minimum0
Maximum97
Zeros12083
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size270.5 KiB
2025-05-31T12:10:56.610936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q323
95-th percentile70
Maximum97
Range97
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.508483
Coefficient of variation (CV)1.2265683
Kurtosis0.5701977
Mean18.350779
Median Absolute Deviation (MAD)12
Skewness1.2670973
Sum634992
Variance506.63182
MonotonicityNot monotonic
2025-05-31T12:10:56.838763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12083
34.9%
1 1176
 
3.4%
21 1030
 
3.0%
19 1015
 
2.9%
22 988
 
2.9%
18 984
 
2.8%
20 970
 
2.8%
23 923
 
2.7%
17 886
 
2.6%
16 691
 
2.0%
Other values (87) 13857
40.0%
ValueCountFrequency (%)
0 12083
34.9%
1 1176
 
3.4%
2 664
 
1.9%
3 531
 
1.5%
4 464
 
1.3%
5 404
 
1.2%
6 374
 
1.1%
7 336
 
1.0%
8 302
 
0.9%
9 261
 
0.8%
ValueCountFrequency (%)
97 1
 
< 0.1%
96 1
 
< 0.1%
94 4
 
< 0.1%
93 2
 
< 0.1%
92 1
 
< 0.1%
91 5
< 0.1%
90 9
< 0.1%
89 8
< 0.1%
88 12
< 0.1%
87 6
< 0.1%

long_nom
Real number (ℝ)

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0940959
Minimum2
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size270.5 KiB
2025-05-31T12:10:57.049471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q16
median7
Q38
95-th percentile11
Maximum38
Range36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1466899
Coefficient of variation (CV)0.30260233
Kurtosis9.6523279
Mean7.0940959
Median Absolute Deviation (MAD)1
Skewness1.8880713
Sum245477
Variance4.6082777
MonotonicityNot monotonic
2025-05-31T12:10:57.236262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7 7690
22.2%
6 7617
22.0%
8 5382
15.6%
5 5074
14.7%
9 3320
9.6%
4 1773
 
5.1%
10 1627
 
4.7%
11 794
 
2.3%
12 339
 
1.0%
3 300
 
0.9%
Other values (18) 687
 
2.0%
ValueCountFrequency (%)
2 9
 
< 0.1%
3 300
 
0.9%
4 1773
 
5.1%
5 5074
14.7%
6 7617
22.0%
7 7690
22.2%
8 5382
15.6%
9 3320
9.6%
10 1627
 
4.7%
11 794
 
2.3%
ValueCountFrequency (%)
38 1
 
< 0.1%
30 1
 
< 0.1%
29 3
 
< 0.1%
27 4
 
< 0.1%
26 4
 
< 0.1%
24 5
 
< 0.1%
23 4
 
< 0.1%
22 14
< 0.1%
21 12
 
< 0.1%
20 31
0.1%

nbre_prenoms
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9848568
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size270.5 KiB
2025-05-31T12:10:57.382140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile3
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.89894686
Coefficient of variation (CV)0.45290263
Kurtosis-0.6588577
Mean1.9848568
Median Absolute Deviation (MAD)1
Skewness0.43765442
Sum68682
Variance0.80810546
MonotonicityNot monotonic
2025-05-31T12:10:57.516296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 12676
36.6%
2 11225
32.4%
3 9359
27.0%
4 1247
 
3.6%
5 88
 
0.3%
6 5
 
< 0.1%
7 3
 
< 0.1%
ValueCountFrequency (%)
1 12676
36.6%
2 11225
32.4%
3 9359
27.0%
4 1247
 
3.6%
5 88
 
0.3%
6 5
 
< 0.1%
7 3
 
< 0.1%
ValueCountFrequency (%)
7 3
 
< 0.1%
6 5
 
< 0.1%
5 88
 
0.3%
4 1247
 
3.6%
3 9359
27.0%
2 11225
32.4%
1 12676
36.6%
Distinct13985
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
Minimum1848-07-15 00:00:00
Maximum1970-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-31T12:10:57.712696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:57.966531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

db_lib_jour
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
Lundi
5104 
Mardi
4999 
Vendredi
4985 
Jeudi
4931 
Samedi
4928 
Other values (2)
9656 

Length

Max length8
Median length6
Mean length6.4117562
Min length5

Characters and Unicode

Total characters221866
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLundi
2nd rowDimanche
3rd rowMardi
4th rowSamedi
5th rowSamedi

Common Values

ValueCountFrequency (%)
Lundi 5104
14.8%
Mardi 4999
14.4%
Vendredi 4985
14.4%
Jeudi 4931
14.3%
Samedi 4928
14.2%
Mercredi 4918
14.2%
Dimanche 4738
13.7%

Length

2025-05-31T12:10:58.197057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T12:10:58.357995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lundi 5104
14.8%
mardi 4999
14.4%
vendredi 4985
14.4%
jeudi 4931
14.3%
samedi 4928
14.2%
mercredi 4918
14.2%
dimanche 4738
13.7%

Most occurring characters

ValueCountFrequency (%)
d 34850
15.7%
i 34603
15.6%
e 34403
15.5%
r 19820
8.9%
n 14827
6.7%
a 14665
6.6%
u 10035
 
4.5%
M 9917
 
4.5%
m 9666
 
4.4%
c 9656
 
4.4%
Other values (6) 29424
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 221866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 34850
15.7%
i 34603
15.6%
e 34403
15.5%
r 19820
8.9%
n 14827
6.7%
a 14665
6.6%
u 10035
 
4.5%
M 9917
 
4.5%
m 9666
 
4.4%
c 9656
 
4.4%
Other values (6) 29424
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 221866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 34850
15.7%
i 34603
15.6%
e 34403
15.5%
r 19820
8.9%
n 14827
6.7%
a 14665
6.6%
u 10035
 
4.5%
M 9917
 
4.5%
m 9666
 
4.4%
c 9656
 
4.4%
Other values (6) 29424
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 221866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 34850
15.7%
i 34603
15.6%
e 34403
15.5%
r 19820
8.9%
n 14827
6.7%
a 14665
6.6%
u 10035
 
4.5%
M 9917
 
4.5%
m 9666
 
4.4%
c 9656
 
4.4%
Other values (6) 29424
13.3%

db_week
Real number (ℝ)

Distinct53
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.909343
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size270.5 KiB
2025-05-31T12:10:58.593008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median25
Q339
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.341462
Coefficient of variation (CV)0.59212084
Kurtosis-1.218183
Mean25.909343
Median Absolute Deviation (MAD)13
Skewness0.061281186
Sum896541
Variance235.36046
MonotonicityNot monotonic
2025-05-31T12:10:58.826064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 961
 
2.8%
1 950
 
2.7%
52 808
 
2.3%
9 772
 
2.2%
2 740
 
2.1%
10 725
 
2.1%
20 721
 
2.1%
13 718
 
2.1%
7 711
 
2.1%
6 708
 
2.0%
Other values (43) 26789
77.4%
ValueCountFrequency (%)
1 950
2.7%
2 740
2.1%
3 685
2.0%
4 682
2.0%
5 665
1.9%
6 708
2.0%
7 711
2.1%
8 681
2.0%
9 772
2.2%
10 725
2.1%
ValueCountFrequency (%)
53 181
 
0.5%
52 808
2.3%
51 623
1.8%
50 638
1.8%
49 642
1.9%
48 637
1.8%
47 574
1.7%
46 589
1.7%
45 590
1.7%
44 570
1.6%

db_code_commune
Real number (ℝ)

High correlation 

Distinct6300
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57799.95
Minimum1004
Maximum99501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size270.5 KiB
2025-05-31T12:10:59.163446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1004
5-th percentile10033
Q133128
median59606
Q378586
95-th percentile99122
Maximum99501
Range98497
Interquartile range (IQR)45458

Descriptive statistics

Standard deviation28932.605
Coefficient of variation (CV)0.50056454
Kurtosis-1.0674234
Mean57799.95
Median Absolute Deviation (MAD)23455
Skewness-0.20408177
Sum2.0000517 × 109
Variance8.3709564 × 108
MonotonicityNot monotonic
2025-05-31T12:10:59.393958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99122 2057
 
5.9%
99109 529
 
1.5%
13055 478
 
1.4%
99352 406
 
1.2%
75114 332
 
1.0%
99123 324
 
0.9%
44109 307
 
0.9%
59350 288
 
0.8%
31555 275
 
0.8%
33063 232
 
0.7%
Other values (6290) 29375
84.9%
ValueCountFrequency (%)
1004 6
< 0.1%
1005 1
 
< 0.1%
1017 1
 
< 0.1%
1026 1
 
< 0.1%
1029 1
 
< 0.1%
1032 2
 
< 0.1%
1033 2
 
< 0.1%
1034 7
< 0.1%
1035 1
 
< 0.1%
1036 1
 
< 0.1%
ValueCountFrequency (%)
99501 2
 
< 0.1%
99431 3
 
< 0.1%
99424 3
 
< 0.1%
99417 4
 
< 0.1%
99416 4
 
< 0.1%
99415 1
 
< 0.1%
99408 1
 
< 0.1%
99407 1
 
< 0.1%
99405 4
 
< 0.1%
99404 12
< 0.1%

db_lib_commune
Text

Missing 

Distinct7862
Distinct (%)23.0%
Missing481
Missing (%)1.4%
Memory size270.5 KiB
2025-05-31T12:10:59.781113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length27
Mean length9.380898
Min length2

Characters and Unicode

Total characters320095
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5258 ?
Unique (%)15.4%

Sample

1st rowBOITRON
2nd rowBRIE-COMTE-ROBERT
3rd rowBROU-SUR-CHANTEREINE
4th rowBROU-SUR-CHANTEREINE
5th rowCERNEUX
ValueCountFrequency (%)
paris 1708
 
4.6%
la 705
 
1.9%
le 683
 
1.8%
lyon 517
 
1.4%
marseille 479
 
1.3%
varsovie 465
 
1.2%
nantes 307
 
0.8%
lille 288
 
0.8%
saint 281
 
0.8%
toulouse 275
 
0.7%
Other values (8027) 31735
84.8%
2025-05-31T12:11:00.370153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 39520
12.3%
A 30141
 
9.4%
N 27083
 
8.5%
R 24298
 
7.6%
S 23329
 
7.3%
I 22829
 
7.1%
L 22082
 
6.9%
O 19295
 
6.0%
U 14593
 
4.6%
T 13607
 
4.3%
Other values (31) 83318
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 320095
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 39520
12.3%
A 30141
 
9.4%
N 27083
 
8.5%
R 24298
 
7.6%
S 23329
 
7.3%
I 22829
 
7.1%
L 22082
 
6.9%
O 19295
 
6.0%
U 14593
 
4.6%
T 13607
 
4.3%
Other values (31) 83318
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 320095
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 39520
12.3%
A 30141
 
9.4%
N 27083
 
8.5%
R 24298
 
7.6%
S 23329
 
7.3%
I 22829
 
7.1%
L 22082
 
6.9%
O 19295
 
6.0%
U 14593
 
4.6%
T 13607
 
4.3%
Other values (31) 83318
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 320095
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 39520
12.3%
A 30141
 
9.4%
N 27083
 
8.5%
R 24298
 
7.6%
S 23329
 
7.3%
I 22829
 
7.1%
L 22082
 
6.9%
O 19295
 
6.0%
U 14593
 
4.6%
T 13607
 
4.3%
Other values (31) 83318
26.0%

db_code_dpt
Real number (ℝ)

High correlation 

Distinct98
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.586452
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size270.5 KiB
2025-05-31T12:11:00.570691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q133
median59
Q378
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)45

Descriptive statistics

Standard deviation28.937175
Coefficient of variation (CV)0.5024997
Kurtosis-1.066841
Mean57.586452
Median Absolute Deviation (MAD)23
Skewness-0.20121855
Sum1992664
Variance837.3601
MonotonicityNot monotonic
2025-05-31T12:11:00.794473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 4718
 
13.6%
75 2248
 
6.5%
59 1742
 
5.0%
62 899
 
2.6%
19 894
 
2.6%
69 760
 
2.2%
13 732
 
2.1%
57 671
 
1.9%
76 666
 
1.9%
44 653
 
1.9%
Other values (88) 20620
59.6%
ValueCountFrequency (%)
1 277
0.8%
2 357
1.0%
3 188
0.5%
4 40
 
0.1%
5 87
 
0.3%
6 234
0.7%
7 246
0.7%
8 217
0.6%
9 70
 
0.2%
10 174
0.5%
ValueCountFrequency (%)
99 4718
13.6%
98 141
 
0.4%
97 484
 
1.4%
95 99
 
0.3%
94 85
 
0.2%
93 147
 
0.4%
92 162
 
0.5%
91 72
 
0.2%
90 53
 
0.2%
89 197
 
0.6%

db_dept_isocode_3166
Text

Missing 

Distinct94
Distinct (%)0.3%
Missing5472
Missing (%)15.8%
Memory size270.5 KiB
2025-05-31T12:11:01.201267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters145655
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFR-77
2nd rowFR-77
3rd rowFR-77
4th rowFR-77
5th rowFR-77
ValueCountFrequency (%)
fr-75 2248
 
7.7%
fr-59 1742
 
6.0%
fr-62 899
 
3.1%
fr-19 894
 
3.1%
fr-69 760
 
2.6%
fr-13 732
 
2.5%
fr-57 671
 
2.3%
fr-76 666
 
2.3%
fr-44 653
 
2.2%
fr-33 610
 
2.1%
Other values (84) 19256
66.1%
2025-05-31T12:11:01.730087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 29131
20.0%
R 29131
20.0%
- 29131
20.0%
5 8461
 
5.8%
7 7872
 
5.4%
3 6067
 
4.2%
6 5990
 
4.1%
9 5518
 
3.8%
1 5494
 
3.8%
2 5442
 
3.7%
Other values (3) 13418
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 145655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 29131
20.0%
R 29131
20.0%
- 29131
20.0%
5 8461
 
5.8%
7 7872
 
5.4%
3 6067
 
4.2%
6 5990
 
4.1%
9 5518
 
3.8%
1 5494
 
3.8%
2 5442
 
3.7%
Other values (3) 13418
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 145655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 29131
20.0%
R 29131
20.0%
- 29131
20.0%
5 8461
 
5.8%
7 7872
 
5.4%
3 6067
 
4.2%
6 5990
 
4.1%
9 5518
 
3.8%
1 5494
 
3.8%
2 5442
 
3.7%
Other values (3) 13418
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 145655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 29131
20.0%
R 29131
20.0%
- 29131
20.0%
5 8461
 
5.8%
7 7872
 
5.4%
3 6067
 
4.2%
6 5990
 
4.1%
9 5518
 
3.8%
1 5494
 
3.8%
2 5442
 
3.7%
Other values (3) 13418
9.2%
Distinct82
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
2025-05-31T12:11:01.958741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length6
Mean length6.2376384
Min length4

Characters and Unicode

Total characters215841
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.1%

Sample

1st rowFRANCE
2nd rowFRANCE
3rd rowFRANCE
4th rowFRANCE
5th rowFRANCE
ValueCountFrequency (%)
france 29263
83.9%
pologne 2057
 
5.9%
allemagne 529
 
1.5%
algerie 406
 
1.2%
russie 324
 
0.9%
la 232
 
0.7%
réunion 232
 
0.7%
roumanie 219
 
0.6%
turquie 152
 
0.4%
guadeloupe 148
 
0.4%
Other values (84) 1316
 
3.8%
2025-05-31T12:11:02.368671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 35659
16.5%
N 33046
15.3%
A 32196
14.9%
R 31172
14.4%
C 29604
13.7%
F 29276
13.6%
O 5071
 
2.3%
L 4246
 
2.0%
G 3603
 
1.7%
I 2461
 
1.1%
Other values (22) 9507
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 215841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 35659
16.5%
N 33046
15.3%
A 32196
14.9%
R 31172
14.4%
C 29604
13.7%
F 29276
13.6%
O 5071
 
2.3%
L 4246
 
2.0%
G 3603
 
1.7%
I 2461
 
1.1%
Other values (22) 9507
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 215841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 35659
16.5%
N 33046
15.3%
A 32196
14.9%
R 31172
14.4%
C 29604
13.7%
F 29276
13.6%
O 5071
 
2.3%
L 4246
 
2.0%
G 3603
 
1.7%
I 2461
 
1.1%
Other values (22) 9507
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 215841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 35659
16.5%
N 33046
15.3%
A 32196
14.9%
R 31172
14.4%
C 29604
13.7%
F 29276
13.6%
O 5071
 
2.3%
L 4246
 
2.0%
G 3603
 
1.7%
I 2461
 
1.1%
Other values (22) 9507
 
4.4%

db_continent
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
EUROPE
33840 
AFRIQUE
 
521
ASIE
 
202
AMERIQUE
 
38
OCEANIE
 
2

Length

Max length8
Median length6
Mean length6.0056353
Min length4

Characters and Unicode

Total characters207813
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUROPE
2nd rowEUROPE
3rd rowEUROPE
4th rowEUROPE
5th rowEUROPE

Common Values

ValueCountFrequency (%)
EUROPE 33840
97.8%
AFRIQUE 521
 
1.5%
ASIE 202
 
0.6%
AMERIQUE 38
 
0.1%
OCEANIE 2
 
< 0.1%

Length

2025-05-31T12:11:02.542869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T12:11:02.683089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
europe 33840
97.8%
afrique 521
 
1.5%
asie 202
 
0.6%
amerique 38
 
0.1%
oceanie 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 68483
33.0%
U 34399
16.6%
R 34399
16.6%
O 33842
16.3%
P 33840
16.3%
A 763
 
0.4%
I 763
 
0.4%
Q 559
 
0.3%
F 521
 
0.3%
S 202
 
0.1%
Other values (3) 42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 207813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 68483
33.0%
U 34399
16.6%
R 34399
16.6%
O 33842
16.3%
P 33840
16.3%
A 763
 
0.4%
I 763
 
0.4%
Q 559
 
0.3%
F 521
 
0.3%
S 202
 
0.1%
Other values (3) 42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 207813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 68483
33.0%
U 34399
16.6%
R 34399
16.6%
O 33842
16.3%
P 33840
16.3%
A 763
 
0.4%
I 763
 
0.4%
Q 559
 
0.3%
F 521
 
0.3%
S 202
 
0.1%
Other values (3) 42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 207813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 68483
33.0%
U 34399
16.6%
R 34399
16.6%
O 33842
16.3%
P 33840
16.3%
A 763
 
0.4%
I 763
 
0.4%
Q 559
 
0.3%
F 521
 
0.3%
S 202
 
0.1%
Other values (3) 42
 
< 0.1%
Distinct3339
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
Minimum1882-10-01 00:00:00
Maximum1970-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-31T12:11:02.859218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:11:03.079880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

dd_lib_jour
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
Samedi
5297 
Lundi
5210 
Mercredi
5152 
Dimanche
4995 
Mardi
4807 
Other values (2)
9142 

Length

Max length8
Median length6
Mean length6.4245875
Min length5

Characters and Unicode

Total characters222310
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMardi
2nd rowSamedi
3rd rowVendredi
4th rowSamedi
5th rowLundi

Common Values

ValueCountFrequency (%)
Samedi 5297
15.3%
Lundi 5210
15.1%
Mercredi 5152
14.9%
Dimanche 4995
14.4%
Mardi 4807
13.9%
Jeudi 4623
13.4%
Vendredi 4519
13.1%

Length

2025-05-31T12:11:03.292864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T12:11:03.450521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
samedi 5297
15.3%
lundi 5210
15.1%
mercredi 5152
14.9%
dimanche 4995
14.4%
mardi 4807
13.9%
jeudi 4623
13.4%
vendredi 4519
13.1%

Most occurring characters

ValueCountFrequency (%)
i 34603
15.6%
e 34257
15.4%
d 34127
15.4%
r 19630
8.8%
a 15099
6.8%
n 14724
6.6%
m 10292
 
4.6%
c 10147
 
4.6%
M 9959
 
4.5%
u 9833
 
4.4%
Other values (6) 29639
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 222310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 34603
15.6%
e 34257
15.4%
d 34127
15.4%
r 19630
8.8%
a 15099
6.8%
n 14724
6.6%
m 10292
 
4.6%
c 10147
 
4.6%
M 9959
 
4.5%
u 9833
 
4.4%
Other values (6) 29639
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 222310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 34603
15.6%
e 34257
15.4%
d 34127
15.4%
r 19630
8.8%
a 15099
6.8%
n 14724
6.6%
m 10292
 
4.6%
c 10147
 
4.6%
M 9959
 
4.5%
u 9833
 
4.4%
Other values (6) 29639
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 222310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 34603
15.6%
e 34257
15.4%
d 34127
15.4%
r 19630
8.8%
a 15099
6.8%
n 14724
6.6%
m 10292
 
4.6%
c 10147
 
4.6%
M 9959
 
4.5%
u 9833
 
4.4%
Other values (6) 29639
13.3%

dd_week
Real number (ℝ)

Distinct53
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.632055
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size270.5 KiB
2025-05-31T12:11:03.682371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median29
Q340
95-th percentile51
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.043129
Coefficient of variation (CV)0.54440864
Kurtosis-1.1771503
Mean27.632055
Median Absolute Deviation (MAD)13
Skewness-0.082183525
Sum956152
Variance226.29574
MonotonicityNot monotonic
2025-05-31T12:11:03.915644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 1065
 
3.1%
31 979
 
2.8%
33 894
 
2.6%
35 798
 
2.3%
46 774
 
2.2%
40 743
 
2.1%
52 737
 
2.1%
49 728
 
2.1%
30 726
 
2.1%
6 722
 
2.1%
Other values (43) 26437
76.4%
ValueCountFrequency (%)
1 428
1.2%
2 619
1.8%
3 582
1.7%
4 659
1.9%
5 600
1.7%
6 722
2.1%
7 708
2.0%
8 564
1.6%
9 620
1.8%
10 713
2.1%
ValueCountFrequency (%)
53 395
1.1%
52 737
2.1%
51 712
2.1%
50 659
1.9%
49 728
2.1%
48 590
1.7%
47 680
2.0%
46 774
2.2%
45 632
1.8%
44 720
2.1%
Distinct7462
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
2025-05-31T12:11:04.504439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters173015
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4938 ?
Unique (%)14.3%

Sample

1st row75114
2nd row89387
3rd row77243
4th row77055
5th row77305
ValueCountFrequency (%)
99122 3597
 
10.5%
13055 677
 
2.0%
59350 617
 
1.8%
54395 480
 
1.4%
33063 454
 
1.3%
75114 453
 
1.3%
44109 380
 
1.1%
69383 342
 
1.0%
31555 318
 
0.9%
75115 299
 
0.9%
Other values (7451) 26549
77.7%
2025-05-31T12:11:05.131040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 25815
14.9%
2 22402
12.9%
9 21596
12.5%
0 18384
10.6%
5 17608
10.2%
3 17245
10.0%
4 13638
7.9%
6 11863
6.9%
7 11773
6.8%
8 10502
6.1%
Other values (3) 2189
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 25815
14.9%
2 22402
12.9%
9 21596
12.5%
0 18384
10.6%
5 17608
10.2%
3 17245
10.0%
4 13638
7.9%
6 11863
6.9%
7 11773
6.8%
8 10502
6.1%
Other values (3) 2189
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 25815
14.9%
2 22402
12.9%
9 21596
12.5%
0 18384
10.6%
5 17608
10.2%
3 17245
10.0%
4 13638
7.9%
6 11863
6.9%
7 11773
6.8%
8 10502
6.1%
Other values (3) 2189
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 25815
14.9%
2 22402
12.9%
9 21596
12.5%
0 18384
10.6%
5 17608
10.2%
3 17245
10.0%
4 13638
7.9%
6 11863
6.9%
7 11773
6.8%
8 10502
6.1%
Other values (3) 2189
 
1.3%

dd_lib_commune
Text

Missing 

Distinct7133
Distinct (%)21.0%
Missing691
Missing (%)2.0%
Memory size270.5 KiB
2025-05-31T12:11:05.535860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length34
Mean length10.505957
Min length2

Characters and Unicode

Total characters356278
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4601 ?
Unique (%)13.6%

Sample

1st rowPARIS-14E-ARRONDISSEMENT
2nd rowSENS
3rd rowLAGNY-SUR-MARNE
4th rowBROU-SUR-CHANTEREINE
5th rowMONTEREAU-FAULT-YONNE
ValueCountFrequency (%)
varsovie 3597
 
10.1%
le 783
 
2.2%
marseille 688
 
1.9%
la 678
 
1.9%
lille 617
 
1.7%
nancy 480
 
1.3%
bordeaux 454
 
1.3%
paris-14e-arrondissement 453
 
1.3%
nantes 380
 
1.1%
lyon--3e--arrondissement 342
 
1.0%
Other values (7122) 27134
76.2%
2025-05-31T12:11:06.152987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 45929
12.9%
A 31041
 
8.7%
R 30213
 
8.5%
S 29134
 
8.2%
N 28931
 
8.1%
I 25491
 
7.2%
O 21769
 
6.1%
L 21047
 
5.9%
- 18675
 
5.2%
T 14165
 
4.0%
Other values (61) 89883
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 356278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 45929
12.9%
A 31041
 
8.7%
R 30213
 
8.5%
S 29134
 
8.2%
N 28931
 
8.1%
I 25491
 
7.2%
O 21769
 
6.1%
L 21047
 
5.9%
- 18675
 
5.2%
T 14165
 
4.0%
Other values (61) 89883
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 356278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 45929
12.9%
A 31041
 
8.7%
R 30213
 
8.5%
S 29134
 
8.2%
N 28931
 
8.1%
I 25491
 
7.2%
O 21769
 
6.1%
L 21047
 
5.9%
- 18675
 
5.2%
T 14165
 
4.0%
Other values (61) 89883
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 356278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 45929
12.9%
A 31041
 
8.7%
R 30213
 
8.5%
S 29134
 
8.2%
N 28931
 
8.1%
I 25491
 
7.2%
O 21769
 
6.1%
L 21047
 
5.9%
- 18675
 
5.2%
T 14165
 
4.0%
Other values (61) 89883
25.2%
Distinct102
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
2025-05-31T12:11:06.500714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters69206
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row75
2nd row89
3rd row77
4th row77
5th row77
ValueCountFrequency (%)
99 5134
 
15.0%
75 1790
 
5.2%
59 1689
 
4.9%
69 1028
 
3.0%
13 1019
 
3.0%
62 758
 
2.2%
33 722
 
2.1%
44 690
 
2.0%
54 676
 
2.0%
76 615
 
1.8%
Other values (91) 20045
58.7%
2025-05-31T12:11:07.008396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 16804
24.3%
5 7923
11.4%
7 7455
10.8%
3 6794
9.8%
6 5987
 
8.7%
4 5838
 
8.4%
1 5307
 
7.7%
2 4906
 
7.1%
8 4169
 
6.0%
0 3145
 
4.5%
Other values (3) 878
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 16804
24.3%
5 7923
11.4%
7 7455
10.8%
3 6794
9.8%
6 5987
 
8.7%
4 5838
 
8.4%
1 5307
 
7.7%
2 4906
 
7.1%
8 4169
 
6.0%
0 3145
 
4.5%
Other values (3) 878
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 16804
24.3%
5 7923
11.4%
7 7455
10.8%
3 6794
9.8%
6 5987
 
8.7%
4 5838
 
8.4%
1 5307
 
7.7%
2 4906
 
7.1%
8 4169
 
6.0%
0 3145
 
4.5%
Other values (3) 878
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 16804
24.3%
5 7923
11.4%
7 7455
10.8%
3 6794
9.8%
6 5987
 
8.7%
4 5838
 
8.4%
1 5307
 
7.7%
2 4906
 
7.1%
8 4169
 
6.0%
0 3145
 
4.5%
Other values (3) 878
 
1.3%

dd_dept_isocode_3166
Text

Missing 

Distinct96
Distinct (%)0.3%
Missing6334
Missing (%)18.3%
Memory size270.5 KiB
2025-05-31T12:11:07.390588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters141345
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFR-75
2nd rowFR-89
3rd rowFR-77
4th rowFR-77
5th rowFR-77
ValueCountFrequency (%)
fr-75 1790
 
6.3%
fr-59 1689
 
6.0%
fr-69 1028
 
3.6%
fr-13 1019
 
3.6%
fr-62 758
 
2.7%
fr-33 722
 
2.6%
fr-44 690
 
2.4%
fr-54 676
 
2.4%
fr-76 615
 
2.2%
fr-57 536
 
1.9%
Other values (86) 18746
66.3%
2025-05-31T12:11:07.908470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 28269
20.0%
R 28269
20.0%
- 28269
20.0%
5 7923
 
5.6%
7 6845
 
4.8%
3 6794
 
4.8%
6 5987
 
4.2%
9 5904
 
4.2%
4 5838
 
4.1%
1 5307
 
3.8%
Other values (5) 11940
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141345
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 28269
20.0%
R 28269
20.0%
- 28269
20.0%
5 7923
 
5.6%
7 6845
 
4.8%
3 6794
 
4.8%
6 5987
 
4.2%
9 5904
 
4.2%
4 5838
 
4.1%
1 5307
 
3.8%
Other values (5) 11940
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141345
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 28269
20.0%
R 28269
20.0%
- 28269
20.0%
5 7923
 
5.6%
7 6845
 
4.8%
3 6794
 
4.8%
6 5987
 
4.2%
9 5904
 
4.2%
4 5838
 
4.1%
1 5307
 
3.8%
Other values (5) 11940
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141345
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 28269
20.0%
R 28269
20.0%
- 28269
20.0%
5 7923
 
5.6%
7 6845
 
4.8%
3 6794
 
4.8%
6 5987
 
4.2%
9 5904
 
4.2%
4 5838
 
4.1%
1 5307
 
3.8%
Other values (5) 11940
8.4%
Distinct83
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size270.5 KiB
2025-05-31T12:11:08.178707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length6
Mean length6.1927634
Min length4

Characters and Unicode

Total characters214282
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.1%

Sample

1st rowFRANCE
2nd rowFRANCE
3rd rowFRANCE
4th rowFRANCE
5th rowFRANCE
ValueCountFrequency (%)
france 29469
84.9%
pologne 3597
 
10.4%
allemagne 279
 
0.8%
algerie 229
 
0.7%
belgique 109
 
0.3%
suisse 103
 
0.3%
espagne 77
 
0.2%
autriche 65
 
0.2%
maroc 51
 
0.1%
italie 49
 
0.1%
Other values (83) 675
 
1.9%
2025-05-31T12:11:08.643568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 35284
16.5%
N 33819
15.8%
A 31097
14.5%
R 30097
14.0%
C 29768
13.9%
F 29492
13.8%
O 7485
 
3.5%
L 4800
 
2.2%
G 4413
 
2.1%
P 3718
 
1.7%
Other values (21) 4309
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 214282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 35284
16.5%
N 33819
15.8%
A 31097
14.5%
R 30097
14.0%
C 29768
13.9%
F 29492
13.8%
O 7485
 
3.5%
L 4800
 
2.2%
G 4413
 
2.1%
P 3718
 
1.7%
Other values (21) 4309
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 214282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 35284
16.5%
N 33819
15.8%
A 31097
14.5%
R 30097
14.0%
C 29768
13.9%
F 29492
13.8%
O 7485
 
3.5%
L 4800
 
2.2%
G 4413
 
2.1%
P 3718
 
1.7%
Other values (21) 4309
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 214282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 35284
16.5%
N 33819
15.8%
A 31097
14.5%
R 30097
14.0%
C 29768
13.9%
F 29492
13.8%
O 7485
 
3.5%
L 4800
 
2.2%
G 4413
 
2.1%
P 3718
 
1.7%
Other values (21) 4309
 
2.0%

dd_continent
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size270.5 KiB
EUROPE
33948 
AFRIQUE
 
417
AMERIQUE
 
139
ASIE
 
86
OCEANIE
 
12

Length

Max length8
Median length6
Mean length6.0154615
Min length4

Characters and Unicode

Total characters208147
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUROPE
2nd rowEUROPE
3rd rowEUROPE
4th rowEUROPE
5th rowEUROPE

Common Values

ValueCountFrequency (%)
EUROPE 33948
98.1%
AFRIQUE 417
 
1.2%
AMERIQUE 139
 
0.4%
ASIE 86
 
0.2%
OCEANIE 12
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-05-31T12:11:08.825932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T12:11:08.969038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
europe 33948
98.1%
afrique 417
 
1.2%
amerique 139
 
0.4%
asie 86
 
0.2%
oceanie 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 68701
33.0%
U 34504
16.6%
R 34504
16.6%
O 33960
16.3%
P 33948
16.3%
A 654
 
0.3%
I 654
 
0.3%
Q 556
 
0.3%
F 417
 
0.2%
M 139
 
0.1%
Other values (3) 110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 68701
33.0%
U 34504
16.6%
R 34504
16.6%
O 33960
16.3%
P 33948
16.3%
A 654
 
0.3%
I 654
 
0.3%
Q 556
 
0.3%
F 417
 
0.2%
M 139
 
0.1%
Other values (3) 110
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 68701
33.0%
U 34504
16.6%
R 34504
16.6%
O 33960
16.3%
P 33948
16.3%
A 654
 
0.3%
I 654
 
0.3%
Q 556
 
0.3%
F 417
 
0.2%
M 139
 
0.1%
Other values (3) 110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 68701
33.0%
U 34504
16.6%
R 34504
16.6%
O 33960
16.3%
P 33948
16.3%
A 654
 
0.3%
I 654
 
0.3%
Q 556
 
0.3%
F 417
 
0.2%
M 139
 
0.1%
Other values (3) 110
 
0.1%

iso_date
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
0
33235 
1
 
1368

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34603
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 33235
96.0%
1 1368
 
4.0%

Length

2025-05-31T12:11:09.125407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T12:11:09.229738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 33235
96.0%
1 1368
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 33235
96.0%
1 1368
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 33235
96.0%
1 1368
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 33235
96.0%
1 1368
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 33235
96.0%
1 1368
 
4.0%

iso_dpt
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
1
24315 
0
10288 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34603
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 24315
70.3%
0 10288
29.7%

Length

2025-05-31T12:11:09.357449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T12:11:09.460933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 24315
70.3%
0 10288
29.7%

Most occurring characters

ValueCountFrequency (%)
1 24315
70.3%
0 10288
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 24315
70.3%
0 10288
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 24315
70.3%
0 10288
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 24315
70.3%
0 10288
29.7%

iso_commune
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
0
22658 
1
11945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34603
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 22658
65.5%
1 11945
34.5%

Length

2025-05-31T12:11:09.592302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T12:11:09.695543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 22658
65.5%
1 11945
34.5%

Most occurring characters

ValueCountFrequency (%)
0 22658
65.5%
1 11945
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22658
65.5%
1 11945
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22658
65.5%
1 11945
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22658
65.5%
1 11945
34.5%

iso_pays
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.5 KiB
1
30527 
0
4076 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34603
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 30527
88.2%
0 4076
 
11.8%

Length

2025-05-31T12:11:09.827968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-31T12:11:09.935534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 30527
88.2%
0 4076
 
11.8%

Most occurring characters

ValueCountFrequency (%)
1 30527
88.2%
0 4076
 
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 30527
88.2%
0 4076
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 30527
88.2%
0 4076
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 30527
88.2%
0 4076
 
11.8%

distance
Real number (ℝ)

Zeros 

Distinct1452
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4206636.6
Minimum0
Maximum1.2345679 × 108
Zeros12051
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size270.5 KiB
2025-05-31T12:11:10.093872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q394.5
95-th percentile1764.1
Maximum1.2345679 × 108
Range1.2345679 × 108
Interquartile range (IQR)94.5

Descriptive statistics

Standard deviation22397158
Coefficient of variation (CV)5.3242436
Kurtosis24.38842
Mean4206636.6
Median Absolute Deviation (MAD)11
Skewness5.1368288
Sum1.4556225 × 1011
Variance5.0163269 × 1014
MonotonicityNot monotonic
2025-05-31T12:11:10.330315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12051
34.8%
123456790 1179
 
3.4%
3 732
 
2.1%
4 732
 
2.1%
5 657
 
1.9%
6 653
 
1.9%
7 645
 
1.9%
519 476
 
1.4%
8 466
 
1.3%
9 449
 
1.3%
Other values (1442) 16563
47.9%
ValueCountFrequency (%)
0 12051
34.8%
1 16
 
< 0.1%
2 253
 
0.7%
3 732
 
2.1%
4 732
 
2.1%
5 657
 
1.9%
6 653
 
1.9%
7 645
 
1.9%
8 466
 
1.3%
9 449
 
1.3%
ValueCountFrequency (%)
123456790 1179
3.4%
19264 1
 
< 0.1%
18817 1
 
< 0.1%
17169 2
 
< 0.1%
17168 1
 
< 0.1%
17002 1
 
< 0.1%
16912 1
 
< 0.1%
16592 1
 
< 0.1%
15807 1
 
< 0.1%
15692 1
 
< 0.1%

Interactions

2025-05-31T12:10:51.127715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:42.006379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:43.270153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:44.555822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:45.838826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:47.218448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:48.504123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:49.785659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:51.285192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:42.166661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:43.427860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:44.714206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:45.993108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:47.373425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:48.658791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:49.938614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:51.448881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:42.325948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:43.589495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:44.876134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:46.147028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:47.536900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:48.823708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:50.095967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:51.609931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:42.483852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:43.750376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:45.036459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:46.403426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:47.699630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:48.987623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:50.254177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:51.766892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:42.646029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:43.908399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:45.194551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:46.550313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:47.858210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:49.144840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:50.404053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:51.927540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:42.804220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:44.071393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:45.358108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:46.704633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:48.014972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:49.324575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:50.560123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:52.087486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:42.957224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:44.231046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:45.514685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:46.856765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:48.173541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:49.473571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:50.714921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:52.246742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:43.107750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:44.387668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:45.672714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:47.060111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:48.333640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:49.626117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-31T12:10:50.972046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-31T12:11:10.518581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agedb_code_communedb_code_dptdb_continentdb_lib_jourdb_weekdd_continentdd_lib_jourdd_weekdistanceiso_communeiso_dateiso_dptiso_payslong_nomnbre_prenomssexe
age1.0000.1470.1490.0830.0130.0010.0590.0470.1960.3240.1800.1920.2390.4290.033-0.1000.126
db_code_commune0.1471.0000.9990.1640.000-0.0140.0670.046-0.0520.0610.1430.0560.2100.5740.053-0.2150.053
db_code_dpt0.1490.9991.0000.1640.000-0.0130.0670.046-0.0510.0620.1430.0560.2100.5740.053-0.2160.053
db_continent0.0830.1640.1641.0000.0090.0170.3860.0150.0140.1030.0450.0080.0320.2520.0000.0680.027
db_lib_jour0.0130.0000.0000.0091.0000.0150.0000.0470.0070.0040.0150.0180.0090.0000.0000.0030.000
db_week0.001-0.014-0.0130.0170.0151.0000.0060.0070.199-0.0060.0180.0090.0170.0230.005-0.0090.022
dd_continent0.0590.0670.0670.3860.0000.0061.0000.0000.0090.0130.0470.0000.0740.2070.0110.0230.020
dd_lib_jour0.0470.0460.0460.0150.0470.0070.0001.0000.0510.0160.0370.0270.0330.0610.0070.0280.024
dd_week0.196-0.052-0.0510.0140.0070.1990.0090.0511.0000.0610.0610.0350.0580.0830.004-0.0340.021
distance0.3240.0610.0620.1030.004-0.0060.0130.0160.0611.0000.1360.0090.2080.222-0.006-0.0150.009
iso_commune0.1800.1430.1430.0450.0150.0180.0470.0370.0610.1361.0000.0620.4720.2130.0250.0640.031
iso_date0.1920.0560.0560.0080.0180.0090.0000.0270.0350.0090.0621.0000.0610.0300.0080.0620.010
iso_dpt0.2390.2100.2100.0320.0090.0170.0740.0330.0580.2080.4720.0611.0000.1010.0380.0940.024
iso_pays0.4290.5740.5740.2520.0000.0230.2070.0610.0830.2220.2130.0300.1011.0000.1060.2260.000
long_nom0.0330.0530.0530.0000.0000.0050.0110.0070.004-0.0060.0250.0080.0380.1061.000-0.0550.000
nbre_prenoms-0.100-0.215-0.2160.0680.003-0.0090.0230.028-0.034-0.0150.0640.0620.0940.226-0.0551.0000.061
sexe0.1260.0530.0530.0270.0000.0220.0200.0240.0210.0090.0310.0100.0240.0000.0000.0611.000

Missing values

2025-05-31T12:10:52.618177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-31T12:10:53.093187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-31T12:10:53.600068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nomprenomsexeagelong_nomnbre_prenomsdb_datedb_lib_jourdb_weekdb_code_communedb_lib_communedb_code_dptdb_dept_isocode_3166db_paysdb_continentdd_datedd_lib_jourdd_weekdd_code_communedd_lib_communedd_code_dptdd_dept_isocode_3166dd_paysdd_continentiso_dateiso_dptiso_communeiso_paysdistance
0LETERMEPATRICE,EUGENE,OMERM17731953-04-20Lundi1777043BOITRON77FR-77FRANCEEUROPE1970-12-01Mardi4975114PARIS-14E-ARRONDISSEMENT75FR-75FRANCEEUROPE00016.900000e+01
1DEROUTJEAN,DANIELM23621947-06-22Dimanche2577053BRIE-COMTE-ROBERT77FR-77FRANCEEUROPE1970-09-05Samedi3689387SENS89FR-89FRANCEEUROPE00017.500000e+01
2GRAS Y PLASSARDBORIS,GUYM11521969-06-03Mardi2377055BROU-SUR-CHANTEREINE77FR-77FRANCEEUROPE1970-11-06Vendredi4577243LAGNY-SUR-MARNE77FR-77FRANCEEUROPE01015.000000e+00
3LENOIRVALERIE,PAULEF0621970-03-14Samedi1177055BROU-SUR-CHANTEREINE77FR-77FRANCEEUROPE1970-03-14Samedi1177055BROU-SUR-CHANTEREINE77FR-77FRANCEEUROPE11110.000000e+00
4LESZCZUKMARIE-AGNESF0811969-11-01Samedi4477066CERNEUX77FR-77FRANCEEUROPE1970-01-19Lundi477305MONTEREAU-FAULT-YONNE77FR-77FRANCEEUROPE01014.500000e+01
5BOSCHETTICHANTAL,MARGUERITE,MADELEINEF20931950-10-10Mardi4177071CHAINTREAUX77FR-77FRANCEEUROPE1970-11-12Jeudi4677387REMAUVILLE77FR-77FRANCEEUROPE01013.000000e+00
6LE CHARPENTIERJEAN-LUC,GEORGESM151421955-03-18Vendredi1177082CHAMPEAUX77FR-77FRANCEEUROPE1970-07-30Jeudi3177288MELUN77FR-77FRANCEEUROPE01011.200000e+01
7DEMELLIERFRANCIS,MICHELM20921950-02-05Dimanche577083CHAMPS-SUR-MARNE77FR-77FRANCEEUROPE1970-08-17Lundi3499109BERLIN99NaNALLEMAGNEEUROPE00008.650000e+02
8CADELLERENE,EDMOND,ALFREDM24731946-06-21Vendredi2577092LA CHAPELLE-SUR-CRECY77FR-77FRANCEEUROPE1970-12-16Mercredi5177288MELUN77FR-77FRANCEEUROPE01011.234568e+08
9CHOPINETBERNARD,DENISM19821950-10-03Mardi4077099CHATEAU-LANDON77FR-77FRANCEEUROPE1970-03-01Dimanche945252PITHIVIERS45FR-45FRANCEEUROPE00013.400000e+01
nomprenomsexeagelong_nomnbre_prenomsdb_datedb_lib_jourdb_weekdb_code_communedb_lib_communedb_code_dptdb_dept_isocode_3166db_paysdb_continentdd_datedd_lib_jourdd_weekdd_code_communedd_lib_communedd_code_dptdd_dept_isocode_3166dd_paysdd_continentiso_dateiso_dptiso_communeiso_paysdistance
34593COUSIN DE MAUVAISINMIREILLE,PAULEF191921950-05-29Lundi2213055MARSEILLE13FR-13FRANCEEUROPE1970-02-05Jeudi613001AIX-EN-PROVENCE13FR-13FRANCEEUROPE010126.0
34594GUERRIGENEVIEVE,MARCELLE,FERNANDE,PHILOMENEF20641950-05-08Lundi1913055MARSEILLE13FR-13FRANCEEUROPE1970-07-26Dimanche3069238SAINT-SYMPHORIEN-SUR-COISE69FR-69FRANCEEUROPE0001269.0
34595LEBARBENCHONMONIQUE,CHANTAL,GENEVIEVEF191231950-05-06Samedi1813055MARSEILLE13FR-13FRANCEEUROPE1970-03-05Jeudi1013055MARSEILLE13FR-13FRANCEEUROPE01110.0
34596BAUDRYERIC,JEAN-MARIEM20621950-07-21Vendredi2913055MARSEILLE13FR-13FRANCEEUROPE1970-12-13Dimanche5013055MARSEILLE13FR-13FRANCEEUROPE01110.0
34597DE PASSORIO PEYSSARDMICHEL,HENRI,MARCELM202031950-07-16Dimanche2813055MARSEILLE13FR-13FRANCEEUROPE1970-10-09Vendredi4113055MARSEILLE13FR-13FRANCEEUROPE01110.0
34598ALBERTINIJACQUES,JOSEPHM19921950-10-08Dimanche4013055MARSEILLE13FR-13FRANCEEUROPE1970-05-15Vendredi2013055MARSEILLE13FR-13FRANCEEUROPE01110.0
34599DI MARIAALAIN,LEONM19821950-11-21Mardi4713055MARSEILLE13FR-13FRANCEEUROPE1970-09-14Lundi3813055MARSEILLE13FR-13FRANCEEUROPE01110.0
34600BREUZAROBERT,ROGERM19621950-12-15Vendredi5013055MARSEILLE13FR-13FRANCEEUROPE1970-02-19Jeudi813055MARSEILLE13FR-13FRANCEEUROPE01110.0
34601BERNARDJEAN-PAUL,LOUIS,GILBERTM19731951-02-27Mardi913055MARSEILLE13FR-13FRANCEEUROPE1970-11-07Samedi4513055MARSEILLE13FR-13FRANCEEUROPE01110.0
34602BRUNETJEAN,PAUL,MARIUSM18631951-02-28Mercredi913055MARSEILLE13FR-13FRANCEEUROPE1970-01-16Vendredi313055MARSEILLE13FR-13FRANCEEUROPE01110.0